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Data Analytics - Data Warehouse 101

By Shree Bharadwaj posted 11-15-2019 11:17


What is a data warehouse?
Data warehouse is an analytical data store that combines information from various sources across the organization and third-party sources, providing a uniform structure for subsequent analyses. A well-designed data warehouse can be leveraged to drive strategic and tactical decisions within an organization.

What do you need to know before creating a data warehouse?
When building a data warehouse, product owners (business intelligence leads) need to consider short-term and long-term business objectives. They need to think about the data lifecycle—how data relate to business processes and how data must be updated with business events. After defining goals and communicating with business stakeholders, product owners and data architects can begin with prototypes or initial versions of a warehouse, using information gathered from historical sources, often in the form of relational databases. By conducting initial analyses and exploring the data landscape, data scientists and architects can define conceptual data models, rules for data transformation, and core metrics for the data warehouse. 

Before implementing the data warehouse, there should be reference documentation, including software specifications for batch, interactive or streaming applications. To the extent possible, plans should include automated pipelines for these applications.

Cross-functional teams and business stakeholders can review documentation and specifications, ensuring that business objectives are met.

While preparing to implement the data warehouse, online training, on-site tutorials, and webinars may help to inform members of the business value of the data warehouse, enabling and enriching a data-driven culture throughout the organization.

What are best practices for data warehouse architecture?
The team building the data warehouse should have good knowledge of data modeling techniques, data governance, business intelligence and event-driven architectures. After the architecture is in place, there should be a detailed end-to-end data flow diagram that identifies data sources and data provenance for traceability.

In today’s competitive landscape, many decisions must be made in near real-time. Companies need to aggressively mine data to inform business decisions. It is important that data be verified and accessible. 

What are some of the innovative ways to leverage a data warehouse?
Leveraging the power of internal as well as third-party datasets, organizations can identify and compute real-time metrics for customer satisfaction, competitively pricing products, forecasting demand, managing inventory, and assessing brand equity, to name just a few applications. Relevant data and improved business intelligence can help executives to make informed decisions, directly impacting business top-line and bottom-line objectives.

Shreenidhi Bharadwaj

Instructor, University of Chicago,

Knowledge Engineering, Research Publishers LLC


#Data Analytics
1 comment



11-15-2019 11:32