How to Build a Data Warehouse from Scratch
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Building a Data Warehouse: the Summary
How to build a data warehouse in 7 steps:
- Elicit DWH goals at the company, department, and business user levels.
- Conceptualize DWH features and select the optimal platform.
- Create a business case and develop a project roadmap.
- Design data cleansing and security policies, data models, and the core architecture components.
- Customize the selected DWH platform and develop ETL/ELT pipelines.
- Migrate data to the DWH.
- Tune ETL/ELT pipelines and adjust DWH performance
- Project time: From 3 to 12 months.
- Cost: Starts from $70,000. Feel free to use our online calculator to get a tailored ballpark estimate.
- Team: A project manager, a business analyst, a data warehouse system analyst, a data warehouse solution architect, a data engineer, a quality assurance engineer, a DevOps engineer.
Approaches to Building a Data Warehouse
A typical architecture of a data warehouse solution includes the following layers:
Data source layer
– internal and external data sources.
Staging area
– a temporary area where data transformations take place. Absent if data transformations are performed in the data storage layer.
Data storage layer
– hosts a data warehouse database (a central database for storing a company’s data) and data marts (data warehousing subsets for storing data for a particular business line – finance, marketing, HR, etc.).
Data Warehouse Use Cases
Strategic decision-making
- Strategic reports and dashboards for top management.
- Financial performance data monitoring and benchmarking.
- Financial forecasting and strategic investment planning.
- Profitability analysis of customers and products.
- Strategic sourcing.
- Employee and department performance assessment and planning.
Data sources:
- Business management systems (ERP, CRM, CMS, EAM, PIM, etc.).
- External data (benchmarking data, surveys, public data, etc.).
Budgeting and financial planning
- Multi-user role-based reports and dashboards.
- Consistency of overall corporate planning and planning for specific business areas.
- Budget allocation.
- Financial simulation and scenario considerations, contingency plans for a range of possible events.
Data sources:
- Operational data from functional areas (supply chain, production, marketing and sales, etc.).
- Financial data stores (ERP, financial management system, accounting system, etc.).
Performance management
- Financial and operational performance reports, scorecards and dashboards for managers.
- Organization, department, employee or process performance tracking.
- Identification of business productivity, employee attrition, etc. drivers.
- Performance gaps anticipation, root cause analysis.
- Strategies for performance optimization for sales funnel, marketing campaigns, supply chain, etc.
Data sources: Business management systems (ERP, accounting management, CRM, supply chain management, etc.).
Tactical decision-making
- Tactical dashboards for managers and directors with continuously updated business data.
- Time-sensitive analytical querying to support production planning, inventory planning, logistic management, etc.
Data sources: Management information systems (inventory control, sales and marketing, accounting and finance, production, logistics, fleet, etc.).
Operational (real-time) data warehousing
- Operational dashboards for fast querying of large and granular transactional data in real time.
- Data-driven decision-making in the operational environment (order entry, banking operations, travel reservations, etc.).
- Alerting to the situations requiring immediate attention (risk management, fraud detection, etc.).
- Constantly updated operational forecasts and business outcome simulations in real time.
Data sources: historical and real-time data from transactional data stores.
IoT, telematics, digital twins
- Reacting (e.g., triggering an alert) to particular events or a sequence of events in real time or near real time.
- Detecting event patterns and predicting reactions based on historical IoT data analysis.
- Predictive maintenance.
- Vehicle telematics.
- Smart building.
- Smart devices and wearables.
- Smart metering.
Data sources: IoT devices.
SaaS, XaaS, online services
- Support for data load scalability.
- Instant analytical querying of huge app data volumes.
- Support for machine learning capabilities (personalization, chatbots, etc.).
Data sources: applications, data and backup storage systems.
7 Steps to Building a Data Warehouse from Scratch
The suggested plan is based on devstudio360 experience in data warehousing services and features the usual procedure we follow when implementing a DWH. Notice that the project timeframes are approximate, as the duration of the data warehouse development process depends on a variety of factors, including the complexity and quality of data in source systems, data security requirements, data analytics objectives, etc.
Step 1. Determine the goals
- Discovery of your business objectives (tactical and strategic) to be pursued with the data warehouse development project.
- Identification and prioritization of the company’s, departments’, business users’ expectations and needs from the project.
- Review of the company’s current technological architecture, applications in use, etc.
- Conducting a preliminary data source analysis (data type and structure, volume, sensitivity, etc.).
- Outlining the data warehouse scope and high-level system requirements, including security and compliance requirements: GDPR (for the EU), PDPL (for Saudi Arabia), HIPAA (for the healthcare industry), etc.
Step 2. Develop a concept and choose the platform
- Defining the desired data warehouse solution feature set.
- Choosing the optimal deployment option (on-premises/in-cloud/hybrid).
- Choosing the optimal architectural design approach to building a data warehouse.
- Selecting the data warehouse technologies (DWH database, ETL/ELT tools, data modeling tools, etc.), taking into account:
- Number of data sources and data volume to be loaded into the data warehouse.
- Data flows to be implemented.
- Data security requirements.
Step 3. Create a business case and a project roadmap
Major activities include:
- Defining data warehouse development project scope, budget planning, timeline, etc.
- Scheduling DWH design, development and testing activities.
- Drawing up a data warehouse project scope document, data warehouse solution architecture vision document, data warehouse deployment strategy, testing strategy, project implementation roadmap.
- Developing a risk management plan.
- Estimating efforts for the data warehouse development project, TCO and ROI.
Step 4. Analyze the system and design the architecture
- Detailed analysis of each data source:
- Data type and structure (data models, if any).
- Data volume generated daily.
- Degree of data sensitivity and an applied data access approach.
- Data quality, missing/poor data, possibility to perform data cleansing in the data source system.
- Identification if any data is absent/of enough quality to support the business requirements.
- Frequency of data updates.
- Relation to other data sources.
- Designing data cleansing policies.
- Creating data security policies (data access policies based on legal restrictions and data security rules, data encryption policies, policies for data access monitoring and data compliance, data backup strategy, etc.)
- Designing data models for the data warehouse and data marts.
- Identifying data objects as entities or attributes; identifying relationships between entities.
- Mapping data objects into the data warehouse.
- Designing ETL/ELT processes for data integration and data flow control.
Step 5. Develop and stabilize the solution
- Data warehouse platform customization.
- Configuring data security software and implementing data security policies (applying data security policies to data at the row, column, etc. level, developing custom security procedures, and more).
- Developing ETL/ELT pipelines and ETL/ELT testing.
- Data warehouse performance testing.
Step 6. Launch the solution
- Data migration, data quality assessment.
- Introducing the data warehouse to business users.
- Having user acceptance tests.
- Conducting user training sessions and workshops.
Step 7. Ensure after-launch support
- ETL/ELT performance tuning.
- Adjusting data warehouse performance and availability, etc.
- Supporting end users.
Consider Professional Services for Data Warehouse Development
devstudio360 has been providing a full range of data warehouse consulting and development services to help companies build a cost-efficient and scalable data warehouse solution to address their data management and analytics needs. With established project management practices, we drive projects to their goals regardless of time and budget constraints.
Data warehouse consulting
- Data warehouse requirements engineering.
- Business case creation, recommendations on data warehouse price optimization.
- Data warehouse conceptualization and software selection.
- Data warehouse solution architecture design.
- Data governance design.
- Data warehouse system analysis.
- Data modeling and ETL/ELT design.
Data warehouse development
- Data warehouse requirements engineering.
- Data warehouse solution conceptualization and platform selection.
- Data warehouse solution architecture design.
- Data warehouse system analysis.
- Data modeling and ETL/ELT design.
- Data warehouse solution development.
- Data warehouse quality assurance and launch.
- Data warehouse after-launch support.
