Data warehouse best practices
Data warehouses are special data storage tools that businesses use to keep all their data in one centralized location. With a data warehouse, businesses only need to access one piece of software to access their business data, streamlining operations and making data experts’ lives easier.
Usually, data warehouses live on the cloud. That means, instead of the data living on an internal server managed by an internal IT team, a business’s data lives on a remote server managed by a third-party data warehouse vendor.
From the data warehouse, data can be used to power business intelligence analytics and dashboards, which are important tools for businesses trying to become more data-driven.
Businesses that want to stay competitive need business intelligence to survive. They need to put their data to work, using it to power analytics and visualizations. Often, the best way to manage data and keep it accessible is through a data warehouse.
Data warehouses act as an intermediary step between business software and BI analytics. With a data warehouse, businesses can more easily collect and store all of their business data. With all the data in one centralized location, it’s much easier to use it to drive insight through BI analytics.
Data strategies that include data warehouses make it much easier for data analysts to compare and combine data from different sources. This allows for a much more holistic data approach, where all data sources are being leveraged effectively.
However, when a data warehouse is at the core of a business’s data strategy, it’s extremely important that the warehouse is implemented correctly and operated in a way that can best serve the business’s needs.
To effectively build data warehouse strategies, businesses need to know how best to implement and operate a data warehouse. Here are some best practices for creating the best possible data warehouse strategies.
Defining Clear Business Objectives
Before implementing a data warehouse, businesses must define clear objectives regarding how they will use and benefit from it. A well-structured data warehouse should align with key business goals, whether it’s improving reporting accuracy, enabling real-time analytics, or enhancing decision-making. By establishing precise objectives, businesses can ensure that their data warehouse strategy supports operational efficiency and business intelligence needs.
Stakeholder Engagement in Data Warehouse Design
Engaging business stakeholders early in the data warehouse design process ensures that the solution aligns with user needs and operational objectives. Regular communication with data analysts, executives, and IT teams helps refine data models, reporting structures, and system functionalities. A user-centric approach minimizes rework and enhances the warehouse’s overall value.
Go with a reputable vendor
There are many data warehousing solutions on the market, but not all of those solutions will effectively meet the needs of your company and your data strategy.
In a cloud-based data warehouse implementation, the data warehouse isn’t managed in-house. The whole point of the cloud is to get data off-premise and let another company take on the costs of storing and managing it.
That means that businesses end up putting a lot of trust in their third-party data warehouse vendors. Since the third party managing the data warehouse is in control of how your business’s data is managed and stored, they have a lot of power.
The biggest fear when a business goes with an unknown for a data warehouse isn’t that the third-party is malicious, it’s that they’re inept. Smaller, less reputable vendors can’t invest the money in data security and uptime that larger vendors can. That puts your data at risk.
In addition, larger vendors can dedicate the resources to have virtually 100% uptime. That means you can access your data anytime, without worrying if the server’s down. That’s not always the case with less well-known vendors.
When your business is looking for a data warehouse solution, remember that there’s a reason that the market-leading solutions are market-leading.
Optimizing Data Storage and Retrieval
Efficient data storage and retrieval mechanisms are crucial for performance optimization. Businesses should focus on indexing strategies, partitioning large datasets, and using compression techniques to reduce storage costs and enhance query speeds. Data warehouses should be structured in a way that enables fast access to frequently used data while efficiently archiving less critical information.
Designing a Scalable Architecture
A well-designed data warehouse should be scalable to handle growing data volumes without performance degradation. Businesses should anticipate future growth by implementing distributed computing solutions, leveraging cloud-based elasticity, and designing a modular architecture that can accommodate increasing data complexity.
Consider data marts and other federated storage strategies
At larger businesses, employees may send data requests to the data warehouse dozens of times a minute. Since everyone in the organization is storing their data in the same place, they have to go to the same place when they want to access it.
All this can make for a massive strain on your data warehouse’s computational resources. ‘Cloud-based’ doesn’t mean that there are no software limitations, and at larger scales, a single data warehouse can quickly get overwhelmed.
Many larger businesses have implemented ‘data marts’ and other federated storage solutions to decrease the strain on their central data warehouse. Federated strategies introduce new storage levels between the end user and the data warehouse.
A data mart is a separate storage space that exists between the end user and the data warehouse. Companies can place data that relates to a certain department or project in a data mart, and then end users can access the data mart instead of the data warehouse when they need something.
The data mart still accesses the data warehouse periodically to update its data, but these updates come every hour or at a similar rate, which is much more manageable than several times a minute.
A business can then build out data marts for all of their operations, keeping anyone from querying the data warehouse directly. In this setup, requests going to the data warehouse are heavily managed, while any given data mart only has to handle a small percentage of overall business traffic.
Use a BI tool as a data warehouse solution
Many businesses already have a tool that can meet their data warehousing needs. These tools can easily connect to other software solutions, store business data in one centralized location, and then use that data to power BI analytics and visualizations.
This tool is the BI system itself. In many cases, it’s much cheaper and easier to use your already implemented BI tool as a data warehouse instead of investing in a standalone data warehouse solution.
Small and mid-sized businesses can especially use their BI tool to get all of the benefits of a data warehouse without spending the money or dedicating the resources to implementing a full data warehouse solution.
Many BI tools can even work as part of a federated data system. For instance, a business could keep all of their business data, regardless of value, in a standalone data warehouse, and then store their important, actionable data in their BI tool for easier access.
Of course, there are some situations where using a BI tool as a primary data warehouse is not optimal. The largest businesses often outstrip the ability of their BI tools to store data, making third-party, enterprise-scale data warehouses essential.
Ensuring Data Quality and Consistency
Maintaining high data quality is essential for any successful data warehouse. Data cleansing and validation should be prioritized to eliminate duplicate, incomplete, or inaccurate records before they enter the warehouse. Consistency in data formats and definitions across all sources ensures reliable analytics and reporting. Implementing automated data validation techniques and periodic audits can help maintain data integrity over time.
Strengthening Security Measures
Data security is a top priority when managing a data warehouse. Implementing access controls, encryption, and multi-factor authentication can protect sensitive business data. Regular security audits and compliance with industry regulations further reduce vulnerabilities and ensure safe data management practices.
Agile Development for Flexibility and Adaptability
An agile approach to data warehouse development enables businesses to quickly adapt to evolving data needs and changing business requirements. Iterative development cycles, rapid feedback loops, and cross-functional collaboration ensure continuous improvements and faster time-to-value for data-driven initiatives.
Establish data governance rules
With data stored on a remote server owned by a third party, many business leaders believe that cloud-based data warehouses are less secure than other storage solutions. While there are security issues that only cloud-based tools need to worry about, the best line of defense in any data storage strategy is data governance.
Data governance is the name for the processes that control when and how users can access information. In a data warehouse-based setup, data governance is very important. It determines who can access the data warehouse, how they have to do it, and what sort of solutions they have for transmitting that data.
Businesses need clear and consistent data governance rules if they want to effectively manage their data. Not only is this just useful for keeping everything straight, it’s also essential for keeping data safe in a cloud-based system.
With good data governance, a cloud-based data warehouse can be safer than other solutions. Make sure to follow basic online security guidelines like not sharing credentials and always logging out of unattended computers.
ETL/ELT Optimization for Efficient Data Integration
Choosing the right ETL (Extract, Transform, Load) or ELT (Extract, Load, Transform) strategy is critical to streamlining data integration. Businesses should evaluate their specific data needs to determine whether a traditional ETL approach or a modern ELT workflow is more suitable. Optimized ETL/ELT processes reduce latency, improve scalability, and ensure data flows efficiently from source systems to the warehouse.
Automate data updates
Many businesses end up doing more work than they have to in their data warehouse. One way businesses can cut down on data warehouse management and get their employees back to their real jobs is through automatic data updates.
Automatic data updates are very simple; they transfer data between your data warehouse and your other tools automatically, rather than manually. In an automatic system, data can flow from your business tools, to your data warehouse, to your BI dashboards, without any human input.
Sometimes, data updates on a specific schedule, like every hour or every day. This is useful for data that doesn’t update all that frequently. In other setups, data is updated whenever the underlying data in the business tool updates, which is best for data that’s valuable in real-time.
Still, both setups are better than manual updates, which rely on someone remembering to update the data before they use it. With automatic updates, businesses get more accurate data with less work.
Regular Monitoring and Performance Tuning
To maintain an efficient data warehouse, continuous monitoring and performance tuning are necessary. Businesses should track query performance, system resource utilization, and data update cycles to identify bottlenecks and inefficiencies. Regular adjustments, such as indexing optimization and query tuning, help sustain long-term operational effectiveness.
Data warehouses — ensuring data success
Data warehouses are quickly becoming the default data infrastructure for any business that wants to put data to work. Unlike in the past, when data warehouses were finicky and hard to implement, now any business can use a data warehouse solution.
However, businesses still need to be aware of the best data warehouse strategies so that they can implement and operate their data warehouse as painlessly as possible. Otherwise, a data warehouse will just make things more complicated.
If you’re unsure that your business needs a standalone data warehouse solution, you can use a BI tool as a data warehouse. This way, you don’t have to deal with another vendor or implement a new tool. While it’s not a perfect solution for everyone, it’s the best choice for many.
Even if you choose to go with a standalone data warehouse, a BI tool should still be an essential part of your data strategy. BI tools use business data, regardless of where it’s stored, to communicate implications and drive insight.