The world of data management is awash with acronyms, terminology, and nomenclature that can make it difficult to get a foothold on what a business intelligence strategy looks like for the average organization.

Unless you are an analyst with years of experience, you might find that any explanation of the essential components of a BI strategy are quickly overrun with technical jargon. In this article, I’m setting out to explain the two basic components of a BI system, one of which is often the data warehouse.

Component 1: The Data Visualization Tool

Every business intelligence strategy starts with one goal: to have better access to information. Tools like Power BI are important to achieving this goal. Once your data visualization software is pointed at some data, it can display that data in an infinite number of ways. The result is that company information is displayed by concise and interactive dashboards, like this one:

Looks nice, right? These dashboards are easy to point at and say: “I want that”. Unfortunately, Power BI is only half of the equation.

Component 2: The Data Source

Data visualization software is useless without data to visualize. Thankfully, tools like Power BI are extremely adept at connecting to data sources, such as:

• Excel Sheets
• 3^rd-party Cloud Services
• External Databases

… and yes, data warehouses.

The Data Warehouse

I like to think of a data warehouse as the “perfect data source”. A data warehouse is a massive database that:

…Contains every row of data from every department in your organization

Think of all that data being collected by all of the different pieces of software across your company.

That poor data is locked up in isolated databases behind each individual piece of software; wherever that software might be installed. All your customer records are somewhere, your financial transactions somewhere else, and so on.

A data warehouse pulls it all together so there’s no need to access these databases separately.

…Is always up-to-date

Did you know that data analysts spend at least 40% of their time preparing their data? A data warehouse, on the other hand, automatically pulls data from your different systems so that it’s always up to date.

…Is well “joined”

In the BI world, a “join” is when you connect two different data sources based on an attribute they share. It means being able to analyze Customer A’s satisfaction and revenue at the same time; or being able to analyze Store B’s traffic and inventory levels at the same time – even if those metrics come from different systems.

A good data warehouse integrates separate data sources along common attributes automatically. Instead of working with many data sources, you are working with a single data source.

…Is easy to analyze

A lot of source systems contain weird structures or strange column names in their databases that take time to make sense of. A good data warehouse is designed to be understood by a human, not a computer program.

…Works automatically

Once you have decided what, how, and when data should flow into a data warehouse… it just works.  Your source systems constantly feed your data warehouse with fresh data.

4 Reasons You Need a Data Warehouse

1. 40-60% reduction in time to analyze data

Earlier, I linked a study that found that analysts spend about half their time preparing data, rather than analyzing it.

A data warehouse eliminates this waste of time.

2. 100% confidence in your data

When your data source is three Excel documents pulled from a foreign system by a colleague two weeks ago, you’re forgiven for not trusting your data.

When your data source is a data warehouse, you can be confident that your data is clean, current, and complete. These priorities are addressed when the data is automatically transferred into the data warehouse.

3. Higher quality insights

Since your data warehouse is optimized for analysis with things like table joins and proper headings, digging into your data source is easy and natural. This unlocks the ability to ask more sophisticated questions when you are analyzing your data.

4. Better data security

If your data is only accessible from a single point, it is a lot easier to manage who can see what. Leave your data warehouse (or just certain areas of it) open to your employees, and lock down all of your other systems.

How to Create a Data Warehouse

Unfortunately, a data warehouse is not easy to build. Every data warehouse is unique because no two organizations have the exact same collection of databases that must be compiled.

Data warehouses are built using a combination of tools such as:

• Custom scripts that transfer data from one place to another
• Graphic tools that dictate table relationships
• Purpose-built connectors

Writing these scripts, using these tools, and configuring these connectors is a large undertaking. It often takes advanced knowledge of your source systems and other technical concepts way outside the scope of this article.

If you want to get started building a data warehouse, contact Encore. We have a lot of experience connecting complicated systems, like Microsoft Dynamics, to a data warehouse in a way that makes sense for your organization.