For many businesses, the important question that often comes with understanding big data is "Where are we to put all this information?" It's not exactly a simple task, for there are volumes of material that get recorded each and every day. Moreover, it's not like companies can use most database practices today, since it's not always necessary to utilize databases like the ones found in most businesses that handle customers or transactions. Often, it's mere statistical recordkeeping that experts can analyze later. That's why data warehousing is an important factor in making the most of predictive analytics through the use of custom BI solutions.
Not just a dumping ground
While a data warehouse functions similarly to its physical namesake, it doesn't necessarily translate into a place where a business simply stores excess information for big data purposes. Database expert James Serra noted that many companies simply utilize a common database for this function, rendering it into a simple dumping ground for the reams of material they record on a regular basis. That's not practical for many reasons, namely that the amount of data required to make actionable and informed decisions tends to be massive.
"A data warehouse functions more like a curated library than temporary storage space."
Instead, how data warehouses function is that a company extracts data from specific sources on a regular basis. Then, using automation or a team of preparers, the data gets cleaned and properly formatted for placement within the database. In the end, the resulting database is more like a curated library than a temporary storage of information. Scalable Startups notes the design of data warehouses makes it so they process storage and queries in bulk, in part because the information necessary to build a model is often vast and deep.
It is this method of organization and delivery that makes data warehousing extremely important to business looking to embrace big data. It allows a lot of integration and flexibility within the confines of business intelligence. For example, a company can create ad-hoc reports and analysis without needing to interfere with the source systems, especially if they happen to be transactional like MySQL and others. Data warehousing can provide full-fledged reports with a higher degree of accuracy because of the ability to drill down details that an analyst can't find from reading individual bits of information. Finally, the potential for data mining for historical trends to better enable predictive analytics is possible. All of these show that data warehousing is an essential part of big data.
