Data Warehouse 101: Best Practices For Digital Businesses

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Big data and analytics used to be an option—now it’s the bare minimum for digital businesses. This is where a data warehouse comes into play. Data warehouses provide a centralized hub for all data needs. Decision-makers analyze this data, gaining insight into the most optimal way of growing the business and revenue over time. 

Why is a Data Warehouse Important?

Digital businesses such as eCommerce handle large quantities of transactional data. However, accessing data quickly won’t be as effective if the quality is poor. A great emphasis on data quality, profiling, cleansing, and validation is required if businesses want to leverage data for critical decision-making. 

Importance of Data Quality

You get the most value from data warehouses when data is clean and consistent. This standardizes the data for establishing a good Master Data Management (MDM) system. Having an MDM helps you confirm the quality from all sources, reducing the number of anomalies. But, it’s only possible through profiling, cleansing, and validation. 

Data Quality, Profiling, Cleansing, Validation

Data quality has a lot to do with the normalization and denormalization of a database. The former removes redundancies and the latter integrates multiple table data for quicker queries.

Once data is cleansed, businesses need a good ETL (Extract, Transfer, Load) process. It helps businesses visualize, replicate, or create consistent data pipelines from your source to your data warehouse. 

Data Warehouse Best Practices

Now that we know the basics, here are five data warehouse best practices tailor-fit for digital businesses.

Establishing Clear Business requirements

Before attempting to build or design a data warehouse, businesses should look into the following:

  • Aligning goals within each department
  • Determining scope and limitations
  • Finding out what data will be useful for analysis
  • Creating disaster recovery protocols
  • Establishing threat mitigation and detection for each layer
  • Forecasting needs

Designing an Effective Data Warehouse

There are three main attributes that encapsulate what a data warehouse is:

  • Subject-oriented: Analysts from any department can access data from a warehouse specific to their needs. 
  • Non-volatile: Data stored in a warehouse isn’t dynamic.
  • Tome-variant: Data warehouses store historical data, perfect for forecast modeling.

To design an effective data warehouse, you need to do the following:

  • Define what your business needs
  • Set up your physical environments
  • Introduce data modeling
  • Establishing your ETL solution
  • Build OLAP cubes
  • Create the front end
  • Optimize queries according to needs
  • Roll out for end-users

Choosing The Right Data Warehouse Architecture

There are three types of data warehouse architecture to go for—single, two, or three-tier. However, most digital businesses go for three-tier architectures as it solves common connectivity issues of the other types. It’s composed of a source layer, reconciled layer, and the data warehouse layer. This is best for enterprise-wide systems. 

Data Cleaning, Normalization, and Denormalization

Normalization removes redundant data and stores consistent ones in a database. Without normalization, queries like insertion, deletion, and updating can result in issues. It provides a framework for data analysis and reduces the need for restructuring tables. 

Denormalization combines data from multiple sources for quick access. But, you should not use data denormalization in a database that has yet to be normalized. Remember, it allows for faster queries—but this won’t matter if data is redundant.

Implementing Data Integration Processes

Data integration in a data warehouse starts with ETL (extract, transfer, load). Data is extracted from multiple sources and combined through query APIs or pre-built connectors. It’s then transformed or cleaned, ensuring consistency and accuracy. This is where standardization occurs in a data set’s format. Then, the data is validated and further filtered according to business needs. Finally, data is loaded for analytics and reporting for analytics and reporting.

When Should a Digital Business get a Data Warehouse?

Digital businesses should get a data warehouse if they want to achieve the following:

  • Standardized data: Data warehouse cleans and standardizes data into a required format to be used for analytics to gain actionable insights. 
  • Better Decision-Making: Data warehousing helps decision-makers identify what strategies work and how to improve those that don’t. 
  • Cost reduction: Historical data helps businesses optimize business processes, ultimately reducing costs and boosting revenue. 

Key Takeaways

Digital businesses handle large quantities of data. The data can be used for analytics to provide insight into how to further optimize and grow a business. And, data warehousing is the best way to store, access, and analyze this data. 

Here are important details you might’ve missed:

  • Data warehouses profile, clean, and validate data across multiple sources.
  • Data needs to be normalized first for accuracy and consistency and then denormalized for faster querying.
  • Businesses need to establish clear business requirements before designing a data warehouse.

About the Author

Chris Tweten, Marketing Representative of AirOps, your AI data sidekick.

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