Data Warehouse Implementation: Plan, Components, Guidelines & More

Proper data warehouse (DWH) implementation allows you to drill down into the organization’s data. It enables you to conclude from the information. Further, to implement an effective BI tool, an organization needs a well-designed data warehouse first.

The design & implementation of data warehouse deals with building a solution for data integration from many sources. It supports analytical reporting and data analysis. However, a poorly designed data warehouse can expose you to the risk of making strategic decisions based on erroneous conclusions.

This post will discuss how to go about data warehouse implementation, its components, benefits, best practices, resources required, and more.

Table of Contents:

• Need For Data Warehouse Implementation?
• Data Warehouse Implementation Plan
• Components of Data Warehouse Implementation
• Benefits of Data Warehouse Implementation
• Guidelines for Data Warehouse Implementation
• Typical Roles in Data Warehouse Implementation Projects
• Why Partner with Rishabh Software for Data Warehouse Implementation?

Mission-critical systems like ERP, CRM, and more are the backbone of any business. They utilize relational databases for data storage. But as companies scale with acquisitions & mergers or expand into new verticals, visibility becomes an increasing issue. The task of retrieving data from disparate systems & analyzing it is highly time & resource intensive. This visibility issue gradually starts hampering further business growth. Here comes the need for the implementation of a Data Warehouse System.

Did you know?

• The global data warehousing market size will grow at a CAGR of 10.7% and reach $51.18 billion by 2028 from $21.18 billion in 2019, predicts Allied Market Research.
• The cloud data warehouse market will grow at a CAGR of 22.56% from 2021 to 2026 and reach $10.42 billion, suggests a Technavio study.

Here’s a step-by-step data warehouse implementation process based on our experience developing custom DWH solutions across multiple industries. It covers all phases of DWH development – from the consultation to the support & maintenance.

The steps in data warehouse implementation include the following:

• Gather Information
• Ideate the Data Warehouse Solution
• Create a Project Roadmap
• Design the DWH architecture
• Develop & Test
• Deploy the DWH solution
• Provide Post-deployment Support

Read our blog on data warehouse development if you want to create one from scratch. However, some steps may vary depending on project complexity, data quality, data analytics objectives & more.

A standard DWH is based on an RDBMS server which acts as a central information repository. It is surrounded by data warehousing components to make the entire environment functional, manageable & accessible.

The core components of a data warehouse architecture include:

1. The Data Source Layer
2. The Data Staging Layer
3. The Data Storage Layer

Each layer performs specific functions and uses a particular set of Software. To understand each layer in detail, read here.

Data warehousing provides immense advantages to companies of all sizes. Here are some of them:

• Better decision making: Improved analytics & business intelligence capabilities with ready access to all the data in one place & a variety of querying tools to generate ad-hoc reports quickly
• Accurate data management and delivery: One of the essential advantages of using a data warehousing system. It helps store all types of data from different sources onto a single base that can be used for analysis purposes.
• Reducing costs: Helps avoid duplication of work, enables reducing the cost & supports increasing the efficiency of the organization
• Saving time: Users save time previously utilized in retrieving data from multiple sources & the total turnaround time for analytics & reporting.

Being a significant component of a data engineering project, it is more likely to meet roadblocks. However, an experienced tech partner like Rishabh Software can help you navigate through them & support you with the right solution for your business.

Based on our experience, here are some of the proven practices for data warehousing implementation:

• Get management support – DWH development could be both time-consuming & resource-intensive process; it is better to take it up after receiving a sustained commitment from the management team.
• Joint management by IT & business teams – The data warehouse project requires considerable research & coordination with different departments & business units to understand their analytics & reporting expectations, existing processes & more. The input of business professionals immensely helps the IT team to make the design workable & reliable.
• Align project strategy with business goals – Before starting the project, define the objectives & expected outcomes clearly. Check if they will contribute to your business goals, and create the project strategy accordingly.
• Accurate cost estimation & plan – All the stakeholders must understand the extent of expenditure & benefits associated with the data warehouse project. It helps avoid misunderstandings & conflicts of interest as the project progresses.
• Develop incrementally – Incremental & iterative development is usually beneficial in building a data warehouse solution as the final requirement specification is precise. You could start by creating & implementing a data mart for a particular department or business area. Later, build similar systems for other sections within the organization. All data marts can then extract information from the data warehouse.
• Built-in flexibility – The data warehouse architecture should be such that it is adaptable to changes in the future. The organization may need to alter the design of the data warehouse if the business model changes.
• Training – The data warehouse users must be aware of its capabilities & features. They should get adequate training to make the best use of it.

• Project Manager – Leads the IT team, delegates responsibilities, defines DWH project objectives, manages its scope, timely & quality execution, and overall communication.
• Business Analyst – Analyzes & documents requirements, roadblocks, technical limitations, processes, systems, integrations, and more
• DWH System Analyst – Analyzes the quality, volume & dependencies of data sources, provides recommendations to improve data storage, analytics & reporting; and ensures data security & the integrity of data extraction.
• DWH Solution Architect – Designs the data warehouse architecture and creates solutions to match your business requirements best while working on the latest DWH tech.
• Data Engineer – Prepares data for operational or analytical uses by developing data models, structures, flows and more. Builds data pipelines linking different sources to the data warehouse and creates the ETL/ELT processes.
• Quality Assurance Engineer – DWH quality assurance also covers organizational roles & structures over the technology. This engineer analyzes requirements and plans & manages DWH tests, test cases & test data.
• DevOps Engineer – Creating CI/CD pipelines for data releases, managing updates into the database layer, automating processes like data fetching, discovering data changes, updating the DWH, and monitoring DWH security & performance.

We provide a full suite of data warehouse development services that cover migration, design & development, modernization, and support. Our multidisciplinary team is highly skilled & experienced in data modeling, management & governance, and business intelligence.

Here’s a use case from our experience;

Case Study: Data Warehouse Solution for Hospitality Business

Our client, a US-based hospitality giant, used many applications (PMS, POS, analytics, inventory, and more) to manage business data. They wanted to create a data warehouse as the single version of the truth. It would help stakeholders derive valuable insights.

Our team provided consultation, gathered requirements & created a detailed project roadmap. Our data warehouse development process included the following:

• We identified data sources that were more than 20 and understood data models for connected applications.
• Our team created an Operational Data Store (ODS) & implemented a data pipeline to access data from transactional resources.
• We developed ETL processes and an API layer.
• Further, we built an analytical layer, created data marts, and mapped the integration need from third-party applications.
• Our team developed Business Intelligence capabilities using Microsoft SQL Server Analysis Services (SSAS) and Power BI.
• We conducted a comparative analysis and deployed the DWH on the Microsoft Azure Cloud platform.

The DWH solution included features like:

• Improved Business Intelligence
• Intuitive dashboards and easy access to reliable, high-quality data
• Scalable data reporting with a minimal total cost of ownership (TCO) footprint
• Support to drive focused sales & marketing initiatives with the right insights with the centralized data repository.

Key takeaways;

• 50% increase in workflow efficiency
• 40% reduction in data quality issues
• 99% accuracy of business understanding available on-the-go