What’s the Difference Between a Database and a Data Warehouse?

When I work with customers the focus always starts on the outcome they’re looking for; their goals, their objectives. With Business Intelligence (BI) in 2022 that goal is often to have visual dashboards. People are looking to get their raw data into a form they can make sense of with as little effort as possible. Depending on the size and complexity of the business, that’s when the idea of a Data Warehouse comes in.

Data Warehouse is a name that people have heard but often don’t understand. The first question is always “what’s the difference between a Data Warehouse and a database?”.

Data Warehouse is a name that conjures up an image of a massive amount of data stored in one place. You can almost see lots and lots of files all tidied away on miles of shelving. In reality though, that description also fits a database.

What is a Database?

A database is an organised collection of data or structured information, stored electronically, typically either on a server or in a computer. Some small databases may be stored in a single file system, whereas other larger databases may be stored on the cloud. The point of a database is to make it easier for businesses to search, select and store the data they’re holding. Whether that’s data on customers, purchasing, office locations, etc.

Characteristics of a database

  • Offers security and removes redundancy
  • Allow multiple view of the data
  • Allows insulation between programs and data
  • Sharing of data and multi user transaction processing

What is a Data Warehouse?

In comparison to databases, a data warehouse is also a big store of data but it’s what then happens to that data that makes a warehouse valuable. In very simplistic terms this is what happens as you build a Data Warehouse:

You pull raw data in from your different systems and spreadsheets
You find connections or keys to join those sets of data together
You transform it into a format that allows you to build visual dashboards
You make it available securely to people building reports and dashboards

Characteristics of a data warehouse

  • A data warehouse is subject oriented as it offers information related to theme instead of companies’ ongoing operations
  • The data also needs to be stored in the data warehouse in common and unanimously acceptable manner
  • The time horizon for the data warehouse is relatively extensive compared with other operational systems
  • A data warehouse is non-volatile which means the previous data is not erased when new information is entered in it.

Example of Data Warehouse

You might have customer names and addresses in one table. You keep customer orders in a different one. In your Data Warehouse you’ve connected the tables together so you can report on them both at the same time.

It’s less a Data Warehouse and more of a Data Refinery. You’re taking in raw materials and transforming them into something useful. Mathematician Clive Humby might well have had this image in mind when he said “Data is the new oil” back in 2006.

There’s a lot more to it but that’s the essence of what a Data Warehouse does without getting bogged down in how it does it.

Benefits of a data warehouse

Efficiency

A good data warehouse automatically pulls together data from across your business. It joins the data together which reduces the manual effort most businesses put in every day. You can go directly from raw data to a data model ready to use for building reports.

Insight Over Time

A good data warehouse also captures the changing states of pieces of data in snapshots over time.

For example, you might have a sales opportunity that is a quote on Monday. If you sell it on Tuesday, you still might need to look back at some point and see what state it was in on Monday. This approach allows you to go back and see the exact state of things at any point. This helps to understand trends in your data and starts to open the door to more advanced analytics. With good historical data you can start to predict what might happen next.

Single Version of the Truth

Everyone reports from the same curated set of data. This stops people arguing over who’s figures are correct. It also stops people wasting time recreating very similar queries and reports. This leads to much greater trust and confidence in business intelligence. It also makes discussions smoother and more engaged, which can only ever be a good thing.

Data Quality

One of the big complaints about automated reporting is that the “data is wrong”. That’s normally because it’s looking at data that is incomplete or inaccurate. Thankfully, you can do a lot to clean up your data on the way through a Data Warehouse. This irons out some of the quality issues that can cause havoc in reports and damage people’s confidence in BI. It also helps to flag up where quality is an issue so people can get proactive about fixing their data. This is a huge step on the way to creating a healthier data culture!

Data Security

In the world of GDPR it’s vital that people in your business can only see data that they need to do their job. This means you need to aggregate the data that people see so you’re not exposing sensitive or personal data. A Data Warehouse can help you achieve this by breaking the link between the end user and the raw data. For example, your Board gets to see sickness absence stats but not who has actually been off sick and why.

Now what?

Building a data warehouse can be a daunting prospect but there is a huge amount of value to be had from it. It’s work that can be broken down into manageable blocks that deliver value quickly. It won’t be the right fit for everyone but it can be a powerful tool to drive good insight and decision making. Get in touch with us today for more information about how we can help build your data warehouse.

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