5 Best Practices for Data Warehousing
Getting started in data warehousing is a major undertaking, so it's important to consider a few best practices when beginning.
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Data warehousing is a great way to create a vault of valuable business information, but it starts with a few best practices. Investing in a data warehouse can help companies compile and use their statistics effectively over months and years. So what should IT and business leaders know before developing one?
What Is Data Warehousing?
Data warehousing includes pooling information from many sources to facilitate analysis and support business decision-making. Companies use it to compile valuable data and convert it into actionable insights. Data warehousing can also be used to create presentations, such as graphs or charts. It acts as an archive, recording and stockpiling statistics over months and years.
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. Naturally, the cloud is the more popular choice due to convenience, cost, and easy scaling.
A cloud-based data warehouse is the most effective option for most businesses. On-premises warehouses are typically only needed when security is a high concern. For example, a private cybersecurity firm might benefit from the higher level of control gained from building one on in-house servers.
2. Determine ETL vs. ELT Early
IT leaders next must determine what data integration method they will use. Again, it’s crucial to make this choice early in the process since it will impact the architecture of the warehouse and its design.
The choices are ETL (extract, transform, load) and ELT (extract, load, transform). The main difference between these two integration methods is when data is transformed. This happens before going to the server in an ETL model. In an ELT model, transformation occurs after the server loads the data.
The ETL method is older but requires less processing power, making it ideal for on-premises servers. ETL is also a good choice if data security is a high concern. Raw information is not sent to the warehouse, so it can be cleaned or removed as needed beforehand. For example, personally identifying information can be deleted in the transformation process.
ELT is better at handling unstructured data and is generally faster, but it requires more computing power than ETL. As a result, it works well with cloud-based warehouses. Since ELT sends raw information, businesses also get more flexibility about how to use it after it’s loaded.
3. 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. Most of today’s reputable cloud providers offer cybersecurity features businesses can use to protect their information.
Additionally, encryption can also be used to protect sensitive data. Studies show that over 40% of businesses report encrypting vulnerable information about customers and employees.
Businesses handling data containing sensitive or identifiable information should use the ETL integration method to protect users. A careful identity and access management strategy are also crucial. This will control who can access the warehouse and limit what users can do with what’s stored there.
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 should 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 for adapting 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 even help protect the warehouse. For example, anti-phishing training can help prevent data theft and keep employees from accidentally giving away sensitive information.
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-term and short-term goals. Additionally, techniques like partitioning can help break a facility into chunks, making it more modular and flexible.
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-premises servers.
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 data, 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.
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