If you’ve ever struggled with any of the common challenges surrounding data integrity, you know how painful it can be to not have the information you need right at your fingertips. Maybe you’ve even taken the time to research your options for getting cleaner, more accurate data and better insights. Here’s everything you need to know about one of the most popular solutions for data integrity issues: the data warehouse. For even more detail on this topic, download our white paper, When the Data Doesn’t Match Up – How to Get Business Insights You Can Trust.
What is a Data Warehouse?
Like its physical counterpart, the data warehouse is intended to be a massive, comprehensive receptacle that is crucial to the operation of a business. Inside a data warehouse, all an organization’s data – no matter its source or its destination – is housed within a single repository.
A traditional data warehouse copies and stores read-only data from a variety of sources. It will typically use ETL (extract, transform, load) processes to collect the data, allowing business intelligence applications to query and analyze this information. However, “logical data warehouses” are now growing more popular as a way to deal with dynamic streaming data. These systems can provide real-time views of all the relevant data within traditional databases and the cloud, all without having to move or copy the information.
No matter how the data is treated behind the scenes, data warehouses are important because they represent a move towards what’s known as the “single version of the truth” (SVOT). Organizations that have an SVOT can store and access all their data in a clear, consistent manner within a single master data repository.
How are Data Warehouses Different from Databases?
Simply put, all data warehouses are databases, but not all databases are data warehouses. Traditional OLTP (online transaction processing) databases support a high volume of rapid transactions and are usually used with a single application. Data warehouses, on the other hand, are OLAP (online analytical processing) databases, specially designed for handling complex queries. They can incorporate multiple databases at once, and also have another layer on top for data analytics.
Data warehouses are just one part of a constellation of data management techniques, from data lakes to data hubs. Although the various types of data stores could fill another blog post, one term that’s worth highlighting here is the “data mart.” Essentially, a data mart is a specialized version of a data warehouse that’s intended for the use of a single department or team, such as sales or finance.
What are the Benefits of a Data Warehouse?
It’s easy to make a business case for the data warehouse. Some of the benefits include the following:
- Speed: A data warehouse can detect and resolve data inconsistencies in fractions of a second, easily beating manual comparison.
- Accessibility: Rather than scattering data across Excel spreadsheets on different users’ computers, it’s all available and safe within a single location.
- Ease of Use: Even completely non-technical users can run their own queries and generate their own business intelligence reports.
- Accuracy: Unlike manual processes, which always make you wonder if you’ve overlooked something incredibly valuable, a data warehouse gives you the confidence that you have the best information available to you.
How Should You Build a Data Warehouse?
As you can imagine, creating a data warehouse is an intricate, lengthy undertaking, and you need to make sure that you’re doing it for the right reasons. Answering the question of why you need a data warehouse is just as important as how you’re going to do it. Everyone involved in the project should understand how a data warehouse will work to fulfill your business objectives.
Since building a data warehouse is such a massive project, it’s important to go slow and do it incrementally. Technical and non-technical employees and key stakeholders should all have a voice before, during, and after the project. As always, testing will be critical in order to discover potential pitfalls and ensure the essential soundness of the data warehouse.
For the best results, consider bringing in a partner to build, or aid in building, your data warehouse. Datavail’s BI & Analytics team has the experience and expertise to make sure it is built properly – and configured to the specific needs of your business – the first time. Our process includes:
- Evaluating business pain points
- Reviewing high-priority KPIs
- Analyzing data sources
- Technical/functional design of the data warehouse
- Change management planning
- Data governance
- ETL/development of the data warehouse by subject area
EPM applications help measure the business performance. This post will help you choose the best EPM solutions for your organization’s needs and objectives.
It’s 2015 and you can now establish totally respectable MS SQL DBA credibility just by mentioning you have been in the game since SQL Server version 9. You may even get the same gasps of shock from some colleagues that used to be reserved for the version 6 veterans.