Data warehouse strategy: design in 6 steps

Building a data warehouse without a well-defined strategy is inefficient and often leads to significant financial and operational setbacks. According to a McKinsey study, organizations without a clear data strategy usually exceed their budgets by 30% and struggle with data quality issues. These challenges can make it hard to make decisions and improve operations.

At N-iX, we offer data warehousing consulting to build an effective strategy for your enterprise. Let's explore the key components, success stories from our data analytics services experience and steps necessary to create an effective data warehouse strategy that addresses these challenges and positions your business for success.

What is a data warehouse strategy?

An enterprise data warehouse strategy is a practical roadmap for leveraging data warehousing technologies to achieve your business objectives and maintain a competitive edge. A practical strategy should address the following key questions:

• What are the organization's goals for the data warehouse? Understanding whether the focus is on operational efficiency, analytical insights, real-time data, or historical analysis is extremely important for shaping the data warehouse plan.
• How much storage space will you need? Evaluating both current and future data storage requirements is essential to ensure the data warehouse can accommodate growth and changing data volumes.
• Where should your data be stored? Deciding on the storage location involves considering factors like security, cost, and accessibility, which can influence whether data is kept on-site, off-site, or both.
• Where and how can data warehousing be applied within the organization? Identifying the areas within the business that could benefit from data warehousing and understanding the specific applications can help tailor the strategy to the organization's needs.
• What resources are needed to implement a data warehouse? Considering the necessary infrastructure, technical skills, and budget for development, deployment, and maintenance ensures that the data warehouse strategy is feasible and sustainable.
• What should be included in the outline of your data stack? The outline should consist of the ETL process, data integration platforms, data orchestration tools, and data science pipelines for handling data from extraction to analysis.
• What actions should you expect to take on the data within a data warehouse? Actions include data cleaning, integration, transformation, and analysis to ensure data quality and derive insights.

The success stories of building a data warehouse strategy

Transitioning to a data-driven enterprise requires expertise, meticulous planning, and the right technology. Here's our structured approach to help you build a data warehouse from initial discovery to ongoing support.

Developing data warehouse and migrating the cloud

Our client faced inefficiencies and high costs in generating monthly service reports due to a lack of centralized data management and reliance on manual processes and third-party tools. N-iX data team collaborated with the client to migrate their on-premises MS SQL Server infrastructure to GCP. We decommissioned over 20 servers. All of this involved consolidating over 70 operational data sources, four data warehouses, and one data lake into a unified data warehouse on Google Cloud.

N-iX's solutions resulted in:

• Migrating to GCP and decommissioning servers reduced expenses;
• Centralizing data in a unified warehouse enhanced data accessibility and quality;
• Reduced manual tasks and saved approximately 17,000 work hours annually.

Read more: Automation, cloud migration, and cost optimization for a global tech company

Setting up a scalable Big Data analytics platform

The client, a large industrial supply company, needed an effective way to handle large amounts of data, particularly inventory-related costs. To migrate from an on-premise Hadoop Hortonworks environment to AWS, the N-iX team built an AWS-based big data platform from scratch. We extended the existing Teradata solution, which collects data from various systems and generates reports with Business Object and Tableau.

Value delivered by N-iX:

• Integrated over 100 different data sources into a unified data platform, handling terabytes of data and growing daily;
• Significant infrastructure cost reduction through cloud migration;
• A unified data platform is storing all data in one place.

Find out more about: Scalable big data analytics platform for leading industrial supply company

Building an AWS-based data warehouse

We helped Gogo migrate their on-premise data solutions to the AWS cloud, significantly expanding their data processing capacity. Our team built a cloud-based unified data platform that collects structured and unstructured data. We fully migrated their data solutions to the AWS cloud and shut down the on-premises infrastructure. We developed a unified data platform that processes extensive data sets from various sources. Using Data Science and Machine Learning, we built predictive models for antenna health monitoring.

Value delivered by N-iX:

• Reduced operational expenses by eliminating unnecessary servicing and leveraging cloud resources;
• Predictive models allowed Gogo to forecast antenna failures with over 90% accuracy, enabling timely maintenance;
• Accomplished a 75% reduction in the no-fault-found rate, ensuring better equipment performance

Read more about: Big Data analytics for improved maintenance and flawless operation of the in-flight internet

Robust data warehouse strategy: key steps to adopt

Without a well-defined strategy, a data warehouse can quickly become a resource-intensive project that fails to deliver the expected value. At N-iX, we understand the complexities of designing and implementing an effective warehouse strategy. Our approach ensures that every aspect of the data warehouse aligns with your business objectives, providing a scalable, secure, and efficient solution.

Here is a detailed look at our step-by-step approach to creating a successful data warehouse strategy:

1. Determining the goals

At N-iX, the first step in our data warehouse strategy plan involves a comprehensive analysis to identify the tactical and strategic business objectives. We work closely with your enterprise to prioritize the needs and expectations of the company, departments, and individual business users. The most important thing for us is to know that the data warehouse aligns perfectly with your business goals.

We start by reviewing your current technological architecture and the applications in use. This, in turn, helps us understand the existing environment and identify potential constraints or opportunities. Next, we conduct a preliminary analysis of your data sources, examining their type, structure, volume, and sensitivity. This step is crucial to defining the scope of the data warehouse and high-level system requirements, including security and compliance with regulations.

2. Developing a concept

In this step, our specialists define the desired features of the data warehouse solution based on your business needs. We help you choose the most suitable deployment option-whether on-premises, cloud-based, or hybrid-considering control, scalability, and cost efficiency.

Our team selects the optimal architectural design approach for the data warehouse, ensuring it supports efficient data integration and robust analytics. We carefully choose the right technologies, including databases, ETL/ELT, and data modeling tools, based on the number and volume of data sources, data flows, and security requirements. Collaboration with business users, analysts, and solution architects is critical to defining core and advanced functionalities, ensuring the architecture is practical and manageable.

3. Creating a project roadmap

We define the project scope, budget, and timeline, scheduling all necessary design, development, and testing activities in the data warehouse strategy document. Comprehensive documentation-including the project scope, architecture vision, deployment strategy, testing strategy, and implementation roadmap-is prepared to guide the project.

Our data experts develop a risk management plan to identify and mitigate potential risks early. Additionally, we provide detailed estimates of project efforts and estimated return on investment (ROI). Rigorous planning at this stage can significantly reduce the overall project time and budget, ensuring efficient resource use.

4. Designing the architecture

A thorough analysis of each data source is conducted to understand their data type, structure, daily volume, sensitivity, and access approach. This step also involves assessing data quality, identifying missing or poor-quality data, and exploring possibilities for data cleansing.

Our team designs robust data cleansing and security policies to ensure data integrity and protection. We create detailed data models for the data warehouse and data marts, identifying data objects as entities or attributes and mapping them into the data warehouse. Efficient ETL/ELT processes facilitate seamless data integration and flow control.

5. Developing the data warehouse solution

In this process, we customize the data warehouse platform to meet your needs. Data security software is configured, and robust security policies are implemented to protect your data at all levels. We develop and rigorously test ETL/ELT pipelines to ensure they are efficient, reliable, and in charge of handling the required data volumes.

Comprehensive performance testing ensures the data warehouse can handle the required workloads and meets all performance benchmarks. We follow a DevOps-driven iterative development approach to ensure rapid release cycles without compromising on quality.

6. Launching and maintaining

The launch phase involves migrating data and assessing its quality to ensure accuracy. We introduce the data warehouse to business users and conduct thorough user acceptance tests to ensure it meets all business requirements and performs as expected.

Post-launch, we fine-tune ETL/ELT processes to optimize performance and efficiency. Continuous monitoring and adjustments ensure the data warehouse remains performant and highly available. Our team supports end users, addressing issues and ensuring they can fully leverage the data warehouse's capabilities.

Read more: Building a data warehouse: A step-by-step guide

Bottom line

How do you transition from merely having a data warehouse to fully leveraging it for maximum business value? The key lies in fostering collaboration between business and technical teams and aligning tools and processes to produce valuable datasets, analytics models, and semantic layers that are actually used and create value rather than data vanity projects.

If your current strategy falls short in these areas, it may be time to seek professional assistance. Professional services can provide the expertise and resources needed to effectively implement and optimize your enterprise data warehouse strategy.

Contact us to learn more about how we can support your journey toward a robust and effective data warehouse.

At N-iX, we offer comprehensive data warehousing services, from initial assessment and strategy development to deployment and long-term support. With a capable team of over 200 professionals specializing in data technologies like Redshift, BigQuery, and Snowflake, N-iX provides comprehensive data warehousing services. Our track record includes successful data projects with industry giants like Lebara, Gogo, and Vable.