What is data warehousing?
Table of Contents
Definition of data warehousing
From Wikipedia
In computing, a data warehouse (DW or DWH), also known as an enterprise data warehouse (EDW), is a system used for reporting and data analysis, and is considered a core component of business intelligence. DWs are central repositories of integrated data from one or more disparate sources. They store current and historical data in one single place that are used for creating analytical reports for workers throughout the enterprise.
Executive view
Data warehousing refers to building a system that can store large volumes of data from several sources, as a central repository from which all other systems can access the data efficiently. This allows your business to implement a range of data science products, including business intelligence and artificial intelligence.
Data warehousing helps businesses:
reliably and accurately store large volumes of valuable data.
modernise their data usage.
create a central repository of all available data to inform better decision making.
Business function leader view
Data warehousing helps all teams within a business to make better-informed decisions based on accurate insights. If you wish to perform data analysis and data science to make better decisions within your team or to implement intelligent features in your products, you will need data that is stored centrally and efficiently.
You may need this service if:
data insights come from disparate sources so that gaining insight is time-consuming.
you are developing business intelligence for your organisation and need a central repository for historical and current data.
KPIs you should consider measuring for this are:
Reduced time spent analysing data from disparate sources.
Improved conversion rates from actionable insights.
Technical view
Data warehousing helps deliver:
a single repository of integrated data from one or more sources.
a single source of current and historical data.
storage of the data for reports / analytics.
Get this service if you encounter:
- difficulties aggregating data across your organisation.
Key criteria to consider are:
Time and cost to develop and maintain this resource.
Accuracy of data.
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FAQs
What is Data warehousing in metals manufacturing?
For metals manufacturers, a data warehouse addresses a problem that nearly every business in the sector recognises: critical business data is scattered across multiple systems — ERP, production management, quality management, finance, and often spreadsheets — that do not easily communicate with each other. Getting a coherent picture of the business requires manual data extraction and reconciliation, which is slow, error-prone, and consistently too late for the decisions that need to be made.
A data warehouse consolidates this data into a single, integrated repository where it can be queried, analysed, and reported on without manual reconciliation. The result is faster, more accurate reporting and the ability to answer questions across systems — connecting production data to financial data, quality data to order data, inventory data to sales data — that would otherwise require significant manual effort.
What do manufacturers gain from a data warehouse?
The specific benefits of a data warehouse for a metals manufacturer include:
- Real-time or near-real-time production reporting: Know the current state of orders in production, not the state from last night’s batch report.
- Cross-system analysis: Connect quality data to production data to understand which processes, materials, or shifts are associated with quality issues.
- Historical analysis: Understand trends in scrap rates, delivery performance, and material consumption over time, not just the current period.
- Accurate inventory valuation: Real-time view of stock with current cost data for finance reporting.
- Customer performance analysis: Understand order fulfilment performance by customer, product type, and time period.