5 Best Practices for Data Warehousing

A data warehouse is a centralized repository that consolidates data from multiple sources to enable comprehensive analysis and support business decision-making. It stores large volumes of historical data, often spanning months or years, making it accessible for trend analysis, reporting, and informed decision-making across organizations.

Investing in a data warehouse can help companies create a vault of valuable business information. It is a great way to compile and use statistics effectively. What should IT and business leaders know before developing one?

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Creating a data warehouse is a major undertaking, so it’s important to have a few best practices in mind when getting started.

1. Understand That Cloud Is King

One of the first choices businesses must make when creating a data warehouse is whether they will use cloud or on-premises infrastructure. The cloud is the more popular choice due to convenience, cost, and scalability. 

A cloud-based warehouse is the most effective option for most businesses. On-premises warehouses are typically only needed when security is a high concern. The cloud’s constant connectivity and hands-off management approach are convenient. However, it increases the data repository’s visibility and obstructs the IT team’s control, incentivizing hackers.

Tips for Avoiding Downstream Scalability Issues 

• Avoid Vendor Lock In: Whether companies opt for private, public, or hybrid cloud solutions, it’s essential to compare providers carefully before committing. Vendor lock-in can arise from unexpected technical incompatibilities, contractual issues, or overreliance on specific services, making foresight crucial to ensure long-term flexibility. 
• Evaluate Cloud Providers: Businesses should evaluate cloud vendors like AWS, Microsoft Azure, Google Cloud, and Snowflake by considering both their technical capabilities and contractual terms. Each provider offers different pricing models, scalability features, and security measures, so aligning the warehouse's requirements with the provider’s strengths is critical for success.

2. Prioritize Cybersecurity

Regardless of the type of data warehouse a business creates, IT leaders should always prioritize cybersecurity. This applies to cloud-based warehouses as well as on-premise, considering 50% of organizations experienced cyberattacks in 2022 because of a vendor. Fortunately, most reputable cloud providers offer off-the-shelf security features. 

Businesses handling data containing sensitive or identifiable information should use the ETL (extract, transform, load) integration method and combine multiple sources into a single, consistent dataset to protect users. A careful identity and access management strategy is also crucial. This will control who can enter the warehouse and limit what users can do with what’s stored there. 

Tips for Data Anonymization and Encryption

Data exfiltration and double-extortion ransomware attacks are becoming more common, so anonymization and encryption are essential. Studies show that over 40% of businesses report encrypting vulnerable information, meaning 60% do not. 

Attackers can use compromised information to launch follow-up attacks, target consumers, or damage a company’s reputation. The average computer would need 300 trillion years to crack today’s most common encryption algorithms, demonstrating the importance of ciphertext.

3. Determine ETL vs. ELT Early

IT leaders must determine what data integration method they will use. It’s crucial to make this choice early in the process since it will impact warehouse architecture. 

The first choice is ETL, which handled 50% of data warehouse processes in 2023. Since it transforms information in the pipeline to the server, raw information is not sent to the warehouse — it can be cleaned or removed beforehand. It has complex hardware requirements, making it ideal for on-premises servers.

The most common alternative is ELT (extract, load, transform). It is better at handling unstructured data. Since it loads data directly, it is less secure than ETL. However, it can leverage the computational power of cloud-based servers to quickly process massive datasets. Moreover, it reduces the need for on-premises hardware, lowering initial costs. 

EtLT (extract, transform, load, transform) is an emerging hybrid model. It can remove or encrypt sensitive data during the first transformation round, making it easier for organizations to aggregate information from disparate sources without increasing management complexity. They no longer have to sacrifice security for speed when choosing ELT. 

Tips for Cloud or On-Premises Solutions

Companies who think ELT will fit their needs better than ETL or EtLT should opt for a cloud-based infrastructure. Since it leverages the warehouse’s computational resources for transformations, the cloud offers near-limitless processing power scalability. 

ETL has become outdated, so organizations who have invested in its complex hardware requirements should consider EtLT since some processes remain on-premises. At the very least, they should keep their old tech stack for redundancy in case of unexpected downtime.

4. Work Closely With Stakeholders

The technical side of things is important when creating a data warehouse, but so are the stakeholders behind the project. Facilities that don’t meet key stakeholders’ expectations may face backtracking, restructuring, and delays. 

Warehouse developers should communicate well with stakeholders throughout the project. They must ensure the C-suite understands the pros and cons of key choices like on-premise vs. cloud or ETL vs. ELT. Before making any decisions like these, getting a clear idea of what stakeholders will use the data warehouse for is critical. 

Developers should check in with stakeholders regularly and leave room to adapt to any changes they may request. Maintaining plenty of resources and learning materials is also a good idea because it helps team members and stakeholders familiarize themselves with the data warehousing system. 

Offering resources and training can help protect the warehouse. For example, anti-phishing training can help prevent data theft and keep employees from accidentally giving away sensitive information. 

Tips for Securing Stakeholder Buy-In

Understanding the psychology of data visualization can help IT leaders explain decisions and express the importance of changes. A person’s working memory has a limited capacity. Graphs, charts, and images reduce their cognitive load and increase their understanding.

5. Prepare to Scale

Scaling can be a major challenge in data warehousing, but planning for it from the start can simplify things. Even if a business doesn’t think it will need to resize its facility down the road, there is no way to know for sure. It’s best to design the warehouse architecture in a way that allows for flexibility and adaptability. 

Decision-makers should carefully analyze what data the warehouse will process and its complexity. Consider long- and short-term goals. Additionally, techniques like partitioning can help break a facility into chunks, reducing query scan time and enabling parallel processing. 

Opting for a cloud-based data warehouse is often the best choice if there is a likelihood of upscaling down the road. It is easier and cheaper to acquire more storage on the cloud than on on-premise servers. It is also easier to scale back if the market shifts or the IT team’s needs change, enabling companies to avoid straining their budgets. 

Tips for Optimizing Data Warehouse Infrastructure and Performance

• Choosing Between Private, Public, or Hybrid Solutions: A hybrid solution is typically the best for a data warehouse because it is cheaper than a private cloud but remains scalable. Justifying directing some resources in-house is easier when companies can segment information between on-premise and cloud servers as needed.
• Optimizing Data Warehouse Performance: As data volumes grow, optimizing performance becomes crucial. Techniques like compression and partitioning can help businesses reduce storage costs and improve query response times. Additionally, indexing strategies can enhance data retrieval efficiency, and cache optimization can reduce latency for frequently accessed data.

Getting Started in Data Warehousing

These best practices can help IT and business leaders get off on the right foot in data warehousing. These facilities act as hubs and repositories for company information, so creating a well-designed, effective warehouse is essential. Regardless of a business’s unique needs and goals, these tips will help IT leaders design a functional, flexible, and secure operation.