Expert Guide to Data Warehousing: What Are the Uses & Benefits of Data?
Business decisions rely on a variety of things, from market demands to company culture and mission statements. But the top business executives know that data-supported decisions are always going to take the lead. That’s why you see Tesla, Spotify, Nintendo, and other big brands using data analytics to empower their decision-making process.
Despite that, the chances are that you have data from multiple sources waiting to be analyzed and applied to your company. The International Data Corporation (IDC) estimated that 80% of data will be unstructured by 2025, and Forbes reported that 90% of unstructured data is never analyzed. This is up from 80% as recently as 2019, but when you consider the wide range of data sources found across any company, it’s not surprising that large amounts of unstructured data would pile up.
So, how can you use structured data warehousing for better business intelligence that puts you ahead of the competition? Let’s start with the basics, then move on to specific data analysis applications that can help unlock your potential.
What is a Data Warehouse?
Typically structured for organizational purposes, data warehousing (DWH) is defined by Astera as data stored digitally for company reports and searches. It’s most often for advanced reporting on business analytics or accessing data as needed. Data warehouses are meant for robust analysis, so they shouldn’t be confused with operational databases such as Microsoft SQL Server, MongoDB, and Apache Cassandra.
Most databases are for a specific purpose, like daily records of transaction systems, meaning they deal in real-time data exclusively. A data warehouse will store data from multiple sources and include historical data, which translates to more complex reporting for savvy business decisions. The journey of data, from source to warehouse, follows these steps:
- Data source: Think flat files, operation systems, etc. Exact data sources can vary by industry and how they do business. Data pipelines run from the source to staging.
- Staging area: Much like a physical warehouse, this area is for processing incoming data before sending it to be stored in the warehouse.
- Data warehouse: This is the centralized repository of information that’s been sorted and analyzed for easy navigation and reporting.
- Data marts: Divided into departments like sales or operations, data marts help with focused searches and reports for more precise analytics.