Data Warehouse Business Requirements
What does success look like for a data warehouse?
A centralized data warehouse can serve the needs of numerous business units simultaneously. Business users can then leverage a single enterprise data model that serves the needs of multiple business divisions, which will deliver a tremendous amount of value to the organization. Let's explore data warehouse business requirements you should be aware of.
In today’s business environment, turning data into information means having the ability to drive business outcomes. Having relevant information can transform how an organization operates as it moves toward becoming a data-driven business.
Success requires a more sophisticated thought process
Organizations must start to become more sophisticated with their data strategies as they gather and aggregate disparate data to create useful information. Many companies have thought about or implemented data warehouses, but simply having a data warehouse is not enough to solve the complex data challenges of many organizations.
Success depends on asking the right questions of the right people
Organizations must address the following questions:
- What business problems are we trying to solve?
- What benefits can we expect to achieve with a single version of the truth by building an enterprise data model?
- How are we increasing the organization’s capabilities?
- Are we capturing and measuring the details through key performance indicators?
- How do we plan to collect, integrate, manage and visualize the data?
There’s certainly a lot to think about when planning an enterprise data warehouse approach and strategy.
Data Warehouse Business Requirements
The key to success is taking a best practices approach by leveraging a proven methodology to deliver information to the enterprise. This raises yet more questions:
- Which approach is suitable for your organization and business challenges?
- Have you built an effective data warehouse strategy that enables business users to access vast amounts of data in an easy-to-use format?
- What is the proposed framework and methodology you plan to leverage to standardize the development of your data warehouse system?
There are a few data warehouse structures you can use for your business. Each serves a specific purpose in organizing and managing data to facilitate efficient data analysis and decision-making processes within an organization.
Enterprise Data Warehouse (EDW)
The Enterprise Data Warehouse (EDW) is a centralized repository that consolidates data from various sources across an entire organization. It is designed to support complex and comprehensive data analysis and reporting. The main goal of an EDW is to provide a unified, consistent, and historical view of the organization's data for business intelligence and analytics purposes.
Key features of an Enterprise Data Warehouse include:
- Data Integration: EDWs integrate data from diverse sources, including operational systems, transactional databases, external data feeds, and more. This integration is crucial to ensure a comprehensive view of the organization's data.
- Data Cleansing and Transformation: Before data is loaded into the EDW, it undergoes cleansing and transformation processes to ensure data quality and consistency.
- Historical Data Storage: EDWs store historical data, allowing analysts and decision-makers to perform trend analysis and track changes over time.
- Complex Query Support: EDWs are optimized for complex queries and analytical operations, enabling in-depth analysis of data.
- Scalability: As the central repository for an organization's data, an EDW must be scalable to accommodate the ever-growing volume of data.
Operational Data Store (ODS)
An Operational Data Store (ODS) is a real-time or near-real-time database that serves as an intermediary stage between operational systems and the Enterprise Data Warehouse. Its primary function is to capture and integrate data from multiple transactional systems, providing a consistent and up-to-date view of the organization's operational data.
Key features of an Operational Data Store include:
- Real-time Data Integration: ODS continuously integrates data from various operational systems, providing the most recent data to support operational reporting and decision-making processes.
- Data Transformation: Similar to the EDW, an ODS may perform data transformation and cleansing to ensure data quality.
- Simplified Reporting: ODS is designed to simplify and expedite operational reporting and analytics, catering to more immediate business needs.
- Data Temporality: ODS may store data for a relatively shorter duration compared to the EDW, as it focuses on recent and current data.
- Lower Granularity: ODS often contains data at a lower level of granularity compared to the EDW, making it well-suited for operational reporting.
Data Marts are subsets of the Enterprise Data Warehouse that are designed to serve the specific data needs of individual departments, teams, or business units within an organization. They are typically smaller and more focused datasets optimized for specific analytical tasks.
Key features of Data Marts include:
- Data Segmentation: Data Marts are organized based on specific business functions or subject areas, allowing for easier access to relevant data for specific user groups.
- Improved Performance: Since Data Marts contain a smaller amount of data, queries and analyses can be executed more quickly, enhancing performance for end-users.
- Departmental Focus: Each Data Mart caters to the analytical needs of a specific department or group, aligning the data to their unique requirements.
- Decentralized Control: Data Marts may be controlled by individual departments or business units, providing them with more autonomy over their data and analytical processes.
- Data Duplication: To maintain independence and performance, Data Marts may duplicate certain data from the EDW, ensuring that changes in one Data Mart do not affect others.
Success starts with effective requirements gathering
Effective requirements gathering is essential to maximize capabilities and deliver business value early and often. I personally use a proven technique that I’ve refined over the years. The process of future state envisioning along with business modeling enables us to determine what information is needed to meet the business requirements. An enterprise data warehouse implementation is a business exercise, and the requirements are paramount to the success of the project.
Success can only be achieved when all data challenges are accounted for
Data challenges must be accounted for to ensure the organization's data warehouse success. Solving for these technical challenges requires strong solutions architects, data architects and data modelers to work closely together to come up with a well-organized data warehouse.
I enjoy helping organizations achieve success
It’s always exciting to watch an organization transform data into a strategic asset and harness its power as a competitive advantage. As the organization shifts its thinking to become more data-centric, it’s enjoyable to watch the transformation as the organization develops a data-driven culture.
Increasing the analytics capabilities of an organization by leveraging a best practices approach to align business and technical leaders enables success, thanks to the endless benefits of a data-driven enterprise. This only happens with a successful implementation that transforms data into usable information that business leaders can use to plan actions.
Written by Mike Sargo
Co-Founder & Chief Data and Analytics Officer at Data Ideology
Mike Sargo is Chief Data and Analytics Officer and Co-Founder of Data Ideology with over 18 years of experience leading, architecting, implementing, and delivering enterprise analytics, business intelligence, and enterprise data management solutions.