A data warehouse is an organisation that stores raw data, in a form that can be mined and analysed. Data warehouses are often used by companies with large operations to combine data from multiple databases into one central location so it can be more easily accessed for reporting and analysis. The term "data warehouse" was coined in the 1990s, during the early days of e-commerce. A company might have its transactional database where all customer orders and product information are stored, as well as its marketing database which tracks customer preferences or demographics; these two databases would not necessarily be linked together because they're maintained by different departments.
There are typically four stages of data warehouse development:
1. The first stage is known as data acquisition, where the current and historical data is sourced from the various systems and databases that will be used in the data warehouse. This can be a time-consuming and difficult process, as the data needs to be extracted and cleaned up so it's ready for use in the warehouse.
2. The next stage is data integration, where the data is combined into one operational data store. This can be done using a variety of methods, such as ETL (Extract, Transform, Load) tools or by writing custom scripts to join the tables together. Combining data from multiple sources can also be referred to as data marts or data lakes.
3. The third stage is data mining and analysis, where the data is queried and used to extract information. This is typically done by developing reports and visualisations, which will make it easier for enterprise managers to interpret the data and make decisions.
4. The final stage is information delivery, where the results of the analysis are delivered back to all the relevant parties who need them via tools such as websites or business intelligence.
When it comes to choosing the right tools for your data warehouse, there are a few things to consider. Firstly, you need to make sure that the tools are capable of handling the amount of data that you're planning to store which is particularly relevant with cloud infrastructure. They also need to be able to handle the variety of data formats that you're likely to encounter, as well as the various database systems that you'll be using.
Another important consideration is compatibility - the tools need to be able to work with your existing infrastructure and systems. When putting together the initial data warehouse architecture the data storage and analytics capabilities need to be fully considered.
And finally, you need to make sure that the tools are easy to use so that your analysts and business users can get the most out of them.
Once you've acquired the data for your data warehouse, the next step is to integrate it and prepare it for use. This is done by loading the unstructured data into the warehouse and transforming it into a format that's suitable for analysis.
There are a variety of methods that can be used for data loading, such as using ETL (Extract, Transform, Load) tools or by writing custom scripts. Transactional systems often combine different data types which may require the transform and load sections of ETL in order to align information from other transactional databases.
The most important thing is to make sure that the data is loaded in a consistent and reliable manner so that it can be used for reporting and analysis.
Transforming the data is also an important step, as it needs to be converted into a format that's easy to use and understand. This is usually done by splitting the data into separate tables and then creating relationships between them referred to as a relational database.
Extracting and cleansing data for your data warehouse is an important step in making sure that the information is accurate and reliable. This involves extracting the data from the source systems and then cleaning it up so that it's ready for use.
One of the most important things to do is to remove any duplicates, as these can distort the results of any analysis. You should also check for missing or out of range values, especially if they affect calculations or comparisons.
Another important consideration is the data type of each column, as this needs to be correct for the type of analysis that you're performing. It's also important to make sure that null values are present where needed.
By following these simple steps, you can ensure that your data warehouse contains accurate and reliable information.
One important consideration when building a data warehouse is the quality of the data that you're using. Before performing any analysis or creating reports, it's important to check that the information in each table is complete and accurate. This means that you'll need to check that each column has the correct data type and that null values are present where needed.
Another important thing to consider is removing duplicates, as these can skew the results of any analysis. It's also important to check for missing or out of range values, especially if they affect calculations or comparisons. This will ensure that your data warehouse is capable of delivering accurate results.
Once you've loaded and transformed the data into a format that's suitable for analysis, you'll need to query it so that you can extract useful information and statistics. There are various tools and techniques that can be used to query your data warehouse, such as ad hoc reporting, business intelligence software and database systems. Predictive analytics can also be used to analyse your data in more detail and generate reports in a format that's easily accessible by business users.
The most widely used method is by querying the data warehouse with SQL (Structured Query Language), which has become the standard language for accessing relational databases. This makes it easy to combine information from different sources into one data warehouse system so that it can be easily queried and analysed.
There are a number of advantages to having a data warehouse in your company, such as:
- Having a single source of truth for all your data. This makes it easy to consolidate information from different sources and departments into one central repository.
- Being able to quickly and easily extract useful information and statistics for reporting and analysis. This can help you make better business decisions based on accurate data.
- Having a historical record of all your data which can be used for future reference or analysis.
- Being able to identify trends and patterns in your data that can help you improve your business operations.
- Being able to easily integrate any new systems or changes with your data warehouse. This can help you maintain a reliable and accurate source of information for all your reporting needs.
There are also some disadvantages to having a data warehouse, such as:
- It takes time to plan, design and build a data warehouse. This can divert resources away from your day-to-day business operations.
- Large amounts of data need to be extracted and transferred into the data warehouse which can take time and resources.
- The cost of building and maintaining a data warehouse needs to be taken into account. This can affect the profitability of your business.
There are a number of existing examples and best practices for companies that have already built their own data warehouses. One example is the Coca-Cola Company, which has been using a data warehouse for over 15 years.
The Coca-Cola Company's data warehouse is a multi-terabyte system that contains information from all of their business operations around the world. It allows them to quickly and easily extract information for reporting and analysis.
Another example is Walmart, which has been using a data warehouse since the early 1990s. Their data warehouse contains over 2 petabytes of data and includes information from all of their stores and suppliers.
These are just a few examples of companies that have successfully implemented a data warehouse into their business operations. By looking at how other companies have implemented their own data warehouses, you can use this as inspiration to implement your own system in your company.
There are a few tips for maintaining a data warehouse:
- Periodically review and clean up your data to ensure that it is accurate and up-to-date.
- Regularly audit your data to make sure that it is still consistent and reliable.
- Make sure that all new systems or changes are integrated into the data warehouse.
- Maintain a backup of your data in case of system failures or data loss.
> Whether you are using cloud data warehouses or on premises data warehouses it's important that sensitive data is protected.
Although it requires time, resources, planning and design, building your own data warehouse can be highly beneficial to your business. Data warehouses can help you make informed business decisions, identify industry trends and patterns in your data, easily integrate new systems or changes with your current set-up, and provide a reliable source of information for all of your reporting needs.
The future of the data warehouse is looking bright. More and more businesses are realizing the benefits of having a data warehouse and are implementing one into their operations. As technology continues to evolve, so too will the methods and tools used to build and maintain a data warehouse. This means that businesses will have access to more powerful systems that can help them extract and analyze their data more effectively - leading us closer towards an era where we don’t need analysts at all! If you want your company to reap some of these potential rewards, then it's time for you to start moving towards your own data warehouse.
Get in touch with one of our consultants to find out if a Data warehouse would benefit your company.
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