What is a data warehouse? Take care of the central repository for your data

A data warehouse acts as a central hub, organizing this information from various sources for easy analysis. It's like a giant, searchable spreadsheet, allowing you to identify trends, track performance, and make data-driven decisions for the entire organization. However, building and maintaining a data warehouse requires data governance to avoid the costly trap of siloed data and redundant reconstructions. In this article, you will learn about its characteristics, how to avoid costly rebuilds, and how data governance relates to return on investment (ROI).

What is a data warehouse?

Imagine a company as a giant filing cabinet. Over the years, documents pile up everywhere - sales records, marketing campaigns, and financial reports. They're all important, but scattered and messy.

The data warehouse gathers information from all corners of the company - sales figures, customer interactions, website traffic, and more. But here's the cool part: it doesn't just dump everything in one pile. The data warehouse organizes it all into a central, easy-to-access system.

DWH system with CRM, ERP and POS

The power lies in unification. By bringing all this data together, the warehouse allows you to see the bigger picture when you use the right tools. You can identify trends, track performance across departments, and make smarter decisions based on real insights, not guesswork.

In a nutshell

A data warehouse is a centralized repository where companies store large amounts of data from various sources (sales, marketing, finance, etc.).

Data warehouse as a central repsitory for data from various sources (sales, marketing, finance, etc.).

Unified data allows for powerful analytics. You can identify trends, track performance, and make data-driven decisions across the entire organization.

What is a data warehouse? From data sources to data warehouse to analysis, reporting and data mining

What is the difference between a data warehouse and a database?

Data warehouses and databases might sound similar, but they each play distinct roles in the world of information. Here's how they compare:

The daily routine vs. the big picture

An application database is optimized for storing and managing the day-to-day data that keeps a company running.  This could be customer information for processing orders, product details for an online store, or financial transactions. Databases prioritize speed and efficiency to ensure smooth operations.

In contrast, a data warehouse is all about the bigger picture. It takes all data from various databases, cleans it up, and organizes it for analysis.  It's not about the daily grind, but about uncovering trends, identifying patterns, and making informed decisions. Speed is less crucial here; what matters most is the ability to analyze vast amounts of data effectively.

DWH is also basically just a huge database, often built on the exact same technology. The difference is how the data is stored and, more importantly, how it is accessed.

Focus on security vs. accessibility

Application databases prioritize data security.  Since they hold real-time operational data, strict access controls are essential.  Data warehouses generally focus on accessibility for analysts who need to explore historical trends.

In essence, databases and data warehouses work together. Databases handle the daily transactions, while data warehouses provide the context for insightful analysis (it doesn't have to be just historical context. Having all the customer information in one place can provide valuable context.). They're like partners, each playing a crucial role in helping companies make the most of their valuable data.

Rebuilding a data warehouse

We've painted a rosy picture of the data warehouse - a centralized haven for unlocking valuable insights. But the truth is, that building and maintaining a data warehouse can be a complex and frustrating journey.  One major challenge companies face is the repeated need to rebuild due to a data management pitfall.

Data warehouses promise a central location to find all your company's insights. But sometimes, even after building one, companies find themselves lost in a maze of useless data.  Why does this happen?

Imagine a data warehouse like a well-organized filing cabinet. Everything is neatly categorized and easy to find. But over time, things get messy. Constant updates break the well-designed database model, naming conventions are not followed. New information is thrown in without following the filing system. People start using different names for the same things, making it impossible to find what you need.

Illustration of data warehouse as a filing cabinet - organized versus non-organized (pile of papers with random labels).

That's what happens to some data warehouses. New data gets added without proper care, making it difficult to understand and analyze.  Imagine searching for a specific report, but instead of clear folders, you find a pile of papers with random labels.  This is what happens when data quality and consistency are ignored.

Why rebuilding your data warehouse can sink your budget

While the exact cost of building a new data warehouse depends on many factors, building a data warehouse can range from $1 million to billions of dollars.

Building a data warehouse can be a hefty investment. While data governance requires an initial investment in tools and processes, it pales in comparison to the cost of rebuilding a flawed data warehouse.  Think of data governance as a preventative measure that saves you money in the long run.

Long-term data architecture planning
Long-term data architecture planning

But the true value of data governance lies beyond just cost savings. It delivers a measurable return on investment (ROI).

Focus on data governance and optimize existing infrastructure to avoid overspending on new data warehouses.

Petr Mikeška
CEO Dawiso
Petr Mikeška
CEO Dawiso

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