Data warehouse: multiply your company's results

Without a doubt, a data warehouse (DW) is essential for a company that seeks to multiply its results and have an analytical advantage today.

If you have already researched the data analysis process, you will certainly have heard about what a data warehouse is and the advantages of using it in your business.

And if you're still not convinced, maybe you need to understand a little more about a DW . Therefore, we brought its main concepts and how they influence your company's numbers.

Keep reading.

Data warehouse: has a single source of truth

The most basic concept of a data warehouse is one of the most important within a business view: a DW is a special type of database that is optimized for large-volume analytical queries (OLAP - online analytical processing) .

It is formed by the union of all the company's data sources for analytical purposes.

This means that, if implemented well, a data warehouse will bring together all data sources in just one location. This allows marketing, sales and production teams to all work with the same data, as the implemented DW will be the only source of truth for making decisions and business forecasts for this company.

Having a DW as the single source of truth is a concept used to ensure that everyone in the organization makes their business decisions based on the same data.

After all, there's nothing more demotivating in a meeting than the numbers that the marketing team presented don't match those that the sales team had in hand, right?

Internal data is only valuable for decision-making if it is reliable for all the company's stakeholders . Therefore, it is essential that you understand the importance of centralizing data in a data warehouse.

Modern data warehouse: 8 concepts to achieve quality results

Without a doubt, a data warehouse (DW) is essential for a company that seeks to multiply its results and have an analytical advantage today.

There are numerous characteristics and applications that define a data warehouse and are related to its architecture, the types of data stored, and the use and way in which data is extracted, loaded and transformed.

And this entire apparatus, when well implemented and monitored, guarantees excellent results.

Let's look at some main and very modern concepts below that will certainly help you to better understand a DW.

1) Facts and measurements

In a data warehouse, a measure is a property in which calculations can be performed; and a set of measures is a fact table. For example, in practice, a sales order table and a production order table are fact tables.

2) Dimensions

They are attributes of a data warehouse to categorize and contextualize facts and measurements, which allows analysis and reporting of measurements. For example, dimensions could be customers, data, suppliers, or a company's products .

3) Integration

A DW integrates data from various company sources, remember? For example, source A and source B may have different ways of identifying a product X; however, in a DW, there will only be one way to identify that same product.

4) Non-volatile data

Once data enters a DW's storage, it will not be changed.

5) Weather dependent

A DW stores historical data because it aims to explain data trends over time. Therefore, even with a change in the cost of a product, for example, a DW will keep the entire history of changes in the unit cost associated with that product.

6) Data mart

A data mart is a subset of a DW, such as a data file focusing on a specific area or department within the company. For example, you might have a data mart for production, one for sales, one for support, and one for quality.

7) Kimball Approach

Ralph Kimball, precursor of the concepts of data warehouse and business intelligence, describes a DW as the crucial fusion in a company's operation between different data marts, created to respond to the analytical needs of each sector.

8) ELT (extract, load, transform)

ELT is a variation of the ETL (extract, transform and load process). The ELT process extracts the raw data from the company's different sources and loads it into DW. Then, when necessary, the raw data is transformed to be used for analytical purposes.

All of these concepts are applied when you implement a modern data warehouse to automate your business processes towards one goal: the delivery of quality results by the company across all sectors.

Yes, a DW promotes all of this because it guarantees the integrity and quality of the data contained within it.

Quality Data = Quality Results

A data warehouse will only generate value for your organization if the data contained in it is complete and of high quality . No one derives insights and makes decisions based on unreliable information. It shouldn't, at least!  

All the concepts of a data warehouse that you saw here guarantee that certain processes, such as data cleaning, data transformation and integration of different sectors of the company, happen in just one place.

This way, whoever works as a data analyst in your company will bring insights that will make all the difference in decision-making in each sector.

Do you understand the importance of a data warehouse in your company's strategy?

Here at Indicium , we use the best and most advanced data warehouse tools available on the market, in addition to having a team made up of certainly the best professionals specializing in this area in Brazil and United States.

Want to know how you can build a data warehouse for your business strategy?

Take the first step and let's talk!

Get in touch here today and count on our help to make this happen. Let's build a strategy for your company to scale unstoppable growth through Jornada Data Driven .