How to Leverage Data Warehousing in Business IntelligenceOmnePresent Team
Business intelligence, as we all know it today, wouldn’t be possible without the data warehouse.
Data warehousing and business intelligence are terms used to describe the strategy of storing all the company’s data in internal or external databases from various sources with the most specialized in analysis and generating actionable insights through BI tools.
One without the other wouldn’t function.
At its core, business intelligence is the ability to answer complex questions about your data and use those answers to create informed business decisions. To try and do this well, you need a data warehouse, which not only provides a secure way to centralize and store all of your data but also a way to quickly find the answers you wish, once you need them.
And that’s a fairly important role. By 2021, it’s estimated humanity will have produced a complete 175 zettabytes of information. For context, that’s 175,000,000,000 terabytes.
Where does all of this information go? Well, most of it goes within the data warehouses.
Companies use data warehouses to manage transactions, understand their data, and keep it all organized. In short, data warehouses make large amounts of knowledge more usable for organizations of all sizes and kinds. This has made them a linchpin of knowledge pipelines and business intelligence systems the globe over. And understanding how data warehouses work can facilitate you to fulfill the total potential of business intelligence (it’s not as complex as it may seem).
For an extended time, Business Intelligence and Data Warehousing were almost synonymous. You couldn’t do one without the other: for timely analysis of massive historical data, you had to prepare, aggregate, and summarize it during a specific format within a knowledge warehouse.
But this dependency of BI on data warehouse infrastructure had an enormous downside. Historically, data warehouses were or are a chic, scarce resource.
They take months and numerous dollars to set up, and even when in situ, they permit only very specific sorts of analysis. If you wish to ask new questions or process new kinds of data, you face major development efforts.
What is a Data Warehouse?
A data warehouse is a data management system that stores large amounts of information for later use in processing and analysis. You’ll consider it as an outsized warehouse where trucks (i.e., source data) unload their data. That data is then sorted into rows and rows of well-organized shelves that make it easy to seek out exactly what you’re trying to find later.
The biggest innovation data warehouses introduced at their inception, consistent with DW 2.0: The Architecture for the following Generation of knowledge Warehousing was the flexibility to store “integrated granular historical data.”
Breaking that down into human terms, this suggests data warehouses surpass storing data that’s:
- Integrated: They combine data from many databases and data sources.
- Granular: the information they home is highly detailed and may be employed in many various ways.
- Historical: they’ll host a never-ending record of knowledge over years and years.